def test_tee_None(self):
import lale.datasets
from lale.lib.lale import Tee
pca = PCA()
trainable = Tee() >> pca
(train_X, train_y), (test_X, test_y) = lale.datasets.digits_df()
trained = trainable.fit(train_X, train_y)
_ = trained.transform(test_X)
def test_tee_def(self):
import numpy as np
import lale.datasets
from lale.lib.lale import Tee
def check_data(X, y):
self.assertEqual(X.dtypes["x1"], np.float64)
pca = PCA()
trainable = Tee(listener=check_data) >> pca
(train_X, train_y), (test_X, test_y) = lale.datasets.digits_df()
trained = trainable.fit(train_X, train_y)
_ = trained.transform(test_X)
def test_tee_obj(self):
import numpy as np
import lale.datasets
from lale.lib.lale import Tee
class check_data:
def __init__(self, outerSelf):
self._outerSelf = outerSelf
def __call__(self, X, y):
self._outerSelf.assertEqual(X.dtypes["x1"], np.float64)
pca = PCA()
trainable = Tee(listener=check_data(self)) >> pca
(train_X, train_y), (test_X, test_y) = lale.datasets.digits_df()
trained = trainable.fit(train_X, train_y)
_ = trained.transform(test_X)